A method for differential composition and variability analyses (constrained Beta-binomial distribution) that jointly models data count distribution, compositionality, group-specific variability, and proportion mean–variability association, being aware of outliers
A plot of estimates of differential composition (c_) on the x-axis and differential variability (v_) on the y-axis. The error bars represent 95% credible intervals. The dashed lines represent the minimal effect that the hypothesis test is based on. An effect is labelled as significant if bigger than the minimal effect according to the 95% credible interval. Facets represent the covariates in the model. Hypothesis testing is performed by calculating the posterior probability of the composition and variability effects being larger than a specified fold-change threshold represented by the blue box
Plot the relationship between abundance and variability.
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